Understanding meanings and predicting user responses is a highly challenging process that often ends in disappointing results. One reason marketing communications can fail is a lack of insight into the semiotics of and responses prompted by advertisements, packaging, or other marketing content. One reason recommendation systems can fail is the over reliance on techniques such as collaborative filtering technologies which cannot classify users apart from their purchase or web site visitation histories. The issues affecting the prediction and classification of consumer response are driven by shortcomings in current processes for analyzing user affinities.
One way user affinity insights can be obtained is through focus groups, surveys and interviews. These can be helpful in characterizing the consumer overall response to products, packaging, advertisements, and recommendations. However, these processes do not adequately account for how the component elements comprising a finished marketing communication affect users. For example, a favorably received advertisement may be composed of text and an image. Overall response to the advertisement, however, may not be fully optimized because the response to the image used is not fully consistent with the message in the text. These issues become increasingly important and difficult as companies strive to achieve greater personalization in their marketing communications.
Product or search recommendation systems often make recommendations based on previous purchases or searches. Because of this, the scope of these systems is limited to historic user activities. Other factors affecting user response to products are not directly evaluated. For example, if a user has only bought comedic movies, other movies recommended will most likely be other comedies. If the user demonstrates a strong emotional response to artistic expressions that juxtapose the themes of heroism and tragedy, the recommendation system will not account for this.
There is a need to obtain deeper insight into what causes consumer affinities based on the meanings and responses to marketing content, and products. These deeper insights cannot be readily obtained from the current, conventional methods of analysis.